{"id":"W2214473900","doi":"10.5539/ijsp.v5n1p61","title":"Recursive Deviance Information Criterion for the Hidden Markov Model","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Deviance information criterion; Deviance (statistics); Hidden Markov model; Likelihood function; Marginal likelihood; Bayesian information criterion; Computer science; Bayesian probability; Model selection; Algorithm; Mathematics; Machine learning; Artificial intelligence; Statistics; Bayesian inference; Estimation theory","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02155635,0.001462622,0.002966417,0.003205168,0.001714296,0.002418617,0.004259066,0.003210549,0.00550236],"category_scores_gemma":[0.08287652,0.0009358427,0.00221558,0.00201846,0.002962105,0.003929238,0.003214815,0.004304314,0.001355828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002237903,"about_ca_system_score_gemma":0.003722714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006179361,"about_ca_topic_score_gemma":0.004757242,"domain_scores_codex":[0.9884337,0.006437377,0.0008291164,0.001935096,0.001967339,0.0003974053],"domain_scores_gemma":[0.9367093,0.05329831,0.002014647,0.003084747,0.004147364,0.0007456674],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002339343,0.0000938415,0.007422965,0.0006335072,0.0005325942,0.0005920465,0.000549919,0.4990549,0.001914152,0.4121985,0.005503169,0.07127044],"study_design_scores_gemma":[0.00003622519,0.00009032566,0.001240039,0.0001075827,0.00007137767,0.0002542273,0.00004841515,0.7896616,0.0008517533,0.2037495,0.003795508,0.00009338489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008577424,0.0008307801,0.9876699,0.0003585164,0.00005300204,0.0001316856,0.0003685714,0.0002990704,0.001711057],"genre_scores_gemma":[0.3418109,0.001593035,0.6460093,0.0008359816,0.0003520478,0.001214756,0.003646052,0.0007127789,0.003825202],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02155635,"threshold_uncertainty_score":0.1140022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0357763876658552,"score_gpt":0.314657356918596,"score_spread":0.2788809692527408,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}